Ienco, DinoBifet, AlbertPfahringer, BernhardPoncelet, Pascal2015-06-1220142015-06-122014Ienco, D., Bifet, A., Pfahringer, B., & Poncelet, P. (2014). Change detection in categorical evolving data streams. In Procedings of 29th Annual ACM Symposium on Applied Computing, Gyeongju, Korea, March 24-28, 2014 (pp. 792–797). New York, NY, USA: ACM. http://doi.org/10.1145/2554850.2554864978-1-4503-2469-4https://hdl.handle.net/10289/9405Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream.792 - 797application/pdfThis is an author’s accepted version of an article published in Procedings of 29th Annual ACM Symposium on Applied Computing. © 2015 ACM.categorical dataconcept driftsevolving data streamstatical testunsupervised change detectionevolving data streamcategorical dataunsupervised change detectionstatistical testconcept driftsMachine learningConference Contribution10.1145/2554850.2554864